In the field of artificially intelligent computer systems capable of answering questions posed in natural language, cognitive question answering (QA) systems (such as the IBM Watson™ artificially intelligent computer system and/or other natural language question answering systems) process questions posed in natural language to determine answers and associated confidence scores based on knowledge acquired by the QA system. Such cognitive QA systems provide powerful tools that can be used in a variety of different applications or fields, such as financial, medical, scientific research, engineering, software, and the like. While there remain challenges with processing the ever increasing amount of unstructured data (such as, for example, the research data, medical records, clinical trials, etc. in the medical field), there are also significant challenges with evaluating the processing results, such as selecting an answer or conclusion from a large, but finite list of possibilities gathered through random acquisition of deterministic factors from a large, finite body of unknown attributes. For example, the decision support process used to make medical treatment recommendations often relies on patient attributes as query parameters that do not effectively reduce the uncertainty of the treatment recommendations or answers. While cognitive QA systems can provide computational power to assimilate and analyze the meaning and context of structured and unstructured data (such as clinical notes, reports, and key patient information) to generate a wealth of candidate treatment option recommendations, the clinical decision making process can actually be impaired when the most valuable patient attributes are not used to select treatment recommendations. Existing solutions for computer assisted decision-making have been limited to operating with structured data (e.g., Bayesian Network decision support systems) or have been narrowly applied (e.g., using correlation engines to map symptoms to diseases), but such solutions do not prioritize the information acquisition used to optimize the decision-making process in support of decision outcomes, such as treatment recommendations. As a result, the existing solutions for efficiently and accurately processing and evaluating queries against large and complex amounts of unstructured data to improve the quality of generated answers are extremely difficult at a practical level.